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Updated: Sep 9, 2025

Spatial Profiling of Protein and RNA Expression in Tissue: An Approach to Fine-Tune Virtual Microdissection
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DISCO: A DIFFUSION MODEL FOR SPATIAL TRANSCRIPTOMICS DATA COMPLETION.

Ziheng Duan1, Xi Li1, Zhuoyang Zhang2

  • 1University of California, Irvine.

Proceedings. International Conference on Image Processing
|September 5, 2025
PubMed
Summary
This summary is machine-generated.

Spatial transcriptomics data often has missing regions. DISCO (DIffusion model for Spatial transcriptomics data COmpletion) effectively reconstructs this missing data using diffusion models and graph neural networks.

Keywords:
data completenessdiffusion modelregion completionspatial transcriptomics

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Area of Science:

  • Molecular Biology
  • Bioinformatics
  • Genomics

Background:

  • Spatial transcriptomics provides crucial insights into tissue organization and function by analyzing gene expression within its spatial context.
  • Technical limitations in spatial transcriptomics experiments frequently lead to substantial data gaps, impeding comprehensive analysis and biological interpretation.

Purpose of the Study:

  • To introduce DISCO (DIffusion model for Spatial transcriptomics data COmpletion), a novel framework designed to address the challenge of missing data in spatial transcriptomics.
  • To enhance the accuracy and completeness of spatial transcriptomics datasets for improved downstream analyses.

Main Methods:

  • DISCO utilizes a graph neural network-based region encoder to integrate spatial and gene expression data from observed regions.
  • The framework incorporates two diffusion modules: one for predicting the spatial positions of missing regions and another for generating gene expression profiles.
  • Neighboring region information is integrated during inference to ensure biologically coherent and smooth data reconstruction.

Main Results:

  • DISCO demonstrates significant effectiveness in reconstructing large missing data regions across diverse spatial transcriptomics datasets.
  • Validation across multiple sequencing platforms and species confirms the robustness and generalizability of the DISCO framework.
  • The method successfully generates biologically plausible gene expression profiles and spatial layouts for previously unobserved areas.

Conclusions:

  • DISCO offers a powerful solution for completing missing data in spatial transcriptomics, thereby improving data quality and enabling more in-depth biological insights.
  • The open-source implementation of DISCO empowers researchers to enhance their spatial transcriptomics data and advance the field.
  • This framework facilitates more accurate tissue organization and functional analyses by mitigating the impact of data gaps.